{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automatic Scoring with Machine Learning\n",
    "\n",
    "This notebook explores classic machine learning algorithms with vectorized features from the student essays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules and setup notebook\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "plt.rcParams['figure.dpi']= 100\n",
    "\n",
    "import spacy\n",
    "from spacy.lang.en.stop_words import STOP_WORDS\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import ElasticNet, LinearRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score\n",
    "from sklearn.ensemble import ExtraTreesClassifier, RandomForestRegressor\n",
    "from sklearn.feature_selection import SelectKBest, f_classif, f_regression\n",
    "\n",
    "# kappa metric for measuring agreement of automatic to human scores\n",
    "from skll.metrics import kappa\n",
    "from bhkappa import mean_quadratic_weighted_kappa\n",
    "\n",
    "plt.style.use('seaborn-colorblind')\n",
    "\n",
    "# Setup Pandas\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "pd.set_option('display.max_colwidth', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read essay data processed in previous notebook\n",
    "training_set = pd.read_pickle('training_spacy.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lemma</th>\n",
       "      <th>pos</th>\n",
       "      <th>ner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9275</th>\n",
       "      <td>[allow, a, dirigible, to, dock, upon, the, top, of, the, empire, state, building, ,, would, leav...</td>\n",
       "      <td>[VERB, DET, NOUN, PART, VERB, ADP, DET, NOUN, ADP, DET, PROPN, PROPN, PROPN, PUNCT, VERB, VERB, ...</td>\n",
       "      <td>[the Empire State Building, Siberia, a thousand foot, the U. S, German, Hindenburg, Lusted, Lusted]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>[in, the, text, ,, the, feature, of, the, setting, greatly, affect, the, cyclist, ., for, exampl...</td>\n",
       "      <td>[ADP, DET, NOUN, PUNCT, DET, NOUN, ADP, DET, NOUN, ADV, VERB, DET, NOUN, PUNCT, ADP, NOUN, PUNCT...</td>\n",
       "      <td>[kind., Secondly, years earlier, GRAPE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10433</th>\n",
       "      <td>[the, architect, could, not, simply, drop, a, mooring, mast, on, top, of, the, empire, state, bu...</td>\n",
       "      <td>[DET, NOUN, VERB, ADV, ADV, VERB, DET, NOUN, NOUN, ADP, NOUN, ADP, DET, PROPN, PROPN, NOUN, ADJ,...</td>\n",
       "      <td>[Empire State, A thousand-foot]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                     lemma                                                                                                  pos                                                                                                  ner\n",
       "9275   [allow, a, dirigible, to, dock, upon, the, top, of, the, empire, state, building, ,, would, leav...  [VERB, DET, NOUN, PART, VERB, ADP, DET, NOUN, ADP, DET, PROPN, PROPN, PROPN, PUNCT, VERB, VERB, ...  [the Empire State Building, Siberia, a thousand foot, the U. S, German, Hindenburg, Lusted, Lusted]\n",
       "3683   [in, the, text, ,, the, feature, of, the, setting, greatly, affect, the, cyclist, ., for, exampl...  [ADP, DET, NOUN, PUNCT, DET, NOUN, ADP, DET, NOUN, ADV, VERB, DET, NOUN, PUNCT, ADP, NOUN, PUNCT...                                                            [kind., Secondly, years earlier, GRAPE]\n",
       "10433  [the, architect, could, not, simply, drop, a, mooring, mast, on, top, of, the, empire, state, bu...  [DET, NOUN, VERB, ADV, ADV, VERB, DET, NOUN, NOUN, ADP, NOUN, ADP, DET, PROPN, PROPN, NOUN, ADJ,...                                                                      [Empire State, A thousand-foot]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set[['lemma', 'pos', 'ner']].sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate vectorized features from processed essays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A document similarity metric is available from *SpaCy*. In order to make use of it for essay scoring, we need to define a reference. Choosing an average, middle-scoring or aggregate essay would leave the sign of the difference undetermined: If the similarity is worse, does that mean the essay is better or worse? Choosing a low scoring essay would theoretically work, however many of the low scoring essays are very short and are full of spelling and grammatical errors. A high scoring essay would be best, though there is another point to consider. When an essay is written in a unique style, how will it compare? Since there are relatively few high scoring essays, the selection was performed manually and arbitrarily under consideration of a \"representative style\". \n",
    "\n",
    "The selection process remains highly subjective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing time: 0:09:56.060959\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Choose arbitrary essay from highest available target_score for each topic.\n",
    "all other essays will be compared to these. \n",
    "The uncorrected essays will be used since the reference essays should have fewer errors.\n",
    "\"\"\"\n",
    "reference_essays = {1: 161, 2: 3022, 3: 5263, 4: 5341, 5: 7209, 6: 8896, 7: 11796, 8: 12340} # topic: essay_id\n",
    "\n",
    "references = {}\n",
    "\n",
    "t0 = datetime.now()\n",
    "\n",
    "nlp = spacy.load('en')\n",
    "stop_words = set(STOP_WORDS)\n",
    "\n",
    "# generate nlp object for reference essays:\n",
    "for topic, index in reference_essays.items():\n",
    "    references[topic] = nlp(training_set.iloc[index]['essay'])\n",
    "\n",
    "# generate document similarity for each essay compared to topic reference\n",
    "training_set['similarity'] = training_set.apply(lambda row: nlp(row['essay']).similarity(references[row['topic']]), axis=1)\n",
    "\n",
    "t1 = datetime.now()\n",
    "print('Processing time: {}'.format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot document similarity vs target score for each topic\n",
    "topic_number = 0\n",
    "fig, ax = plt.subplots(4,2, figsize=(7,10))\n",
    "for i in range(4):\n",
    "    for j in range(2):\n",
    "        topic_number += 1\n",
    "        sns.regplot(x='target_score', y='similarity', data=training_set[training_set['topic'] == topic_number], ax=ax[i,j])\n",
    "        ax[i,j].set_title('Topic %i' % topic_number)\n",
    "ax[3,0].locator_params(nbins=10)\n",
    "ax[3,1].locator_params(nbins=10)\n",
    "plt.suptitle('Document similarity by topic')\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.savefig('image5.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Document similarity may prove to be ineffective for persuasive/narritive essays. The example below shows the highest scored essay for topic 8. The author uses a unique creative style which is unlikely to be replicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Bell rings.  Shuffle, shuffle. @CAPS1. Snap. EEEE. Crack. Slam. Click, stomp, @CAPS1. Tap tap tap. SLAM. Creak. Shoof, shoof.  Sigh. Seventh class of the day. Here we go. \"@CAPS2! Tu va ou pas? On a +¬tude cette class-l+á. Tu peux aller au bibliotheque si tu veux....\" @CAPS3 all blinked at me, @PERSON1, @NUM1le and @ORGANIZATION1, chocolate-haired and mocha skinned, impatiently awaiting my answer. The truth was, I knew @CAPS3 didn\\'t really care if I came or not. It made no difference to them if I trailed a few feet behind like some pathetic puppy. I was silent but adorable, loved only because I was an @CAPS4. Because I spoke fidgety @CAPS5. Because I was the exchange student, because my translator and colorful clothes were so shocking for ten seconds, and were then forgotten about.  I was a flock of seagulls haircut. So why are you here? I thought. Why did you go on exchange at all? You are the complete opposite of everyone here. No one wants you. Just go home.  But my ego had a ready answer. You begged for this remember? For months and months, it was all you wanted, all you thought about, all you dreamt about. So I went with the girls. As expected, @CAPS3 walked down the three-person wide staircase side-by-side, and I shuffled awkwardly behind them. Finally arriving at @NUM2scalier, we sat at a table, the three girls talking. I glazed my eyes over, attempting to look lost in thought, as if I didn\\'t care I wasn\\'t included. Selfish thoughts buzzed in my head; if @CAPS3 weren\\'t talking to me, why should I make the effort to talk to them?   I really had no idea how @CAPS3 felt about me. How does someone feel about their shadow? @CAPS3 notice it, sure, but it never offers up insight, it never makes you laugh. It\\'s all in the confidence, said my mother\\'s voice, all how you carry yourself. But I knew it wasn\\'t that simple. I was just too alien. These girls would never understand me, as I would never understand them. In frustration, I started to flick peas across the room with my spoon. Pat, flick, sproing.  This caught the interest of @PERSON1, as @NUM1le and @ORGANIZATION1 were discussing something very emotional. Tears began to pour out of @ORGANIZATION1\\'s eyes. Sniffling, she and @NUM1le went to the bathroom, leaving me all alone with @PERSON1. Only @PERSON2 could have felt my felt my same emotion as he stared up at @CAPS6. Silently, I continued shooting peas. @PERSON1 just stared at them as @CAPS3 darted around the room. Suddenly, with a horrible miscalculation, a pea hit a boy in the face. And then, he turned around and swore. And then, @PERSON1 and I looked at each other from across the table.  And then, we laughed.  We laughed so hard I cried. So hard that huge, alien tears flooded from my eyes. People around us were laughing too, even though @CAPS3 had no idea what was so funny. I didn\\'t even know what was so funny. But it didn\\'t matter, because we were dripping tears and snot, reaching for each other, reenacting the pea hitting the boy\\'s face. It was as if we had been friends for years, and laughing happened all the time. It was saturated with all the angst and lonliness and despair I had felt the past four weeks. The connection we felt was instantaneous, like lightening, the kind of connection I felt with my best friends back home. I felt that huge swelling sensation in my chest, like a balloon was stuck inside. My stomach was aching and my cheeks were so sore I felt them seizing up. My heart felt whole even for that second. My soul was open. It was the best laugh of my life.  Sniffle sniffle. GASP. Laughter. GASP. Swipe of tears. Sniffle sniffle. Laughter. GASP. This is why. I thought. This is why you came.  Bell rings.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.iloc[12340]['essay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing time: 0:00:07.245959\n"
     ]
    }
   ],
   "source": [
    "# count various features\n",
    "\n",
    "t0 = datetime.now()\n",
    "\n",
    "training_set['token_count'] = training_set.apply(lambda x: len(x['tokens']), axis=1)\n",
    "training_set['unique_token_count'] = training_set.apply(lambda x: len(set(x['tokens'])), axis=1)\n",
    "training_set['nostop_count'] = training_set \\\n",
    "            .apply(lambda x: len([token for token in x['tokens'] if token not in stop_words]), axis=1)\n",
    "training_set['sent_count'] = training_set.apply(lambda x: len(x['sents']), axis=1)\n",
    "training_set['ner_count'] = training_set.apply(lambda x: len(x['ner']), axis=1)\n",
    "training_set['comma'] = training_set.apply(lambda x: x['corrected'].count(','), axis=1)\n",
    "training_set['question'] = training_set.apply(lambda x: x['corrected'].count('?'), axis=1)\n",
    "training_set['exclamation'] = training_set.apply(lambda x: x['corrected'].count('!'), axis=1)\n",
    "training_set['quotation'] = training_set.apply(lambda x: x['corrected'].count('\"') + x['corrected'].count(\"'\"), axis=1)\n",
    "training_set['organization'] = training_set.apply(lambda x: x['corrected'].count(r'@ORGANIZATION'), axis=1)\n",
    "training_set['caps'] = training_set.apply(lambda x: x['corrected'].count(r'@CAPS'), axis=1)\n",
    "training_set['person'] = training_set.apply(lambda x: x['corrected'].count(r'@PERSON'), axis=1)\n",
    "training_set['location'] = training_set.apply(lambda x: x['corrected'].count(r'@LOCATION'), axis=1)\n",
    "training_set['money'] = training_set.apply(lambda x: x['corrected'].count(r'@MONEY'), axis=1)\n",
    "training_set['time'] = training_set.apply(lambda x: x['corrected'].count(r'@TIME'), axis=1)\n",
    "training_set['date'] = training_set.apply(lambda x: x['corrected'].count(r'@DATE'), axis=1)\n",
    "training_set['percent'] = training_set.apply(lambda x: x['corrected'].count(r'@PERCENT'), axis=1)\n",
    "training_set['noun'] = training_set.apply(lambda x: x['pos'].count('NOUN'), axis=1)\n",
    "training_set['adj'] = training_set.apply(lambda x: x['pos'].count('ADJ'), axis=1)\n",
    "training_set['pron'] = training_set.apply(lambda x: x['pos'].count('PRON'), axis=1)\n",
    "training_set['verb'] = training_set.apply(lambda x: x['pos'].count('VERB'), axis=1)\n",
    "training_set['noun'] = training_set.apply(lambda x: x['pos'].count('NOUN'), axis=1)\n",
    "training_set['cconj'] = training_set.apply(lambda x: x['pos'].count('CCONJ'), axis=1)\n",
    "training_set['adv'] = training_set.apply(lambda x: x['pos'].count('ADV'), axis=1)\n",
    "training_set['det'] = training_set.apply(lambda x: x['pos'].count('DET'), axis=1)\n",
    "training_set['propn'] = training_set.apply(lambda x: x['pos'].count('PROPN'), axis=1)\n",
    "training_set['num'] = training_set.apply(lambda x: x['pos'].count('NUM'), axis=1)\n",
    "training_set['part'] = training_set.apply(lambda x: x['pos'].count('PART'), axis=1)\n",
    "training_set['intj'] = training_set.apply(lambda x: x['pos'].count('INTJ'), axis=1)\n",
    "\n",
    "t1 = datetime.now()\n",
    "print('Processing time: {}'.format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to file\n",
    "training_set.to_pickle('training_features.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = pd.read_pickle('training_features.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the generated features are correlated with essay length. Collinearity is potentially an issue here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 970.5x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot correlation of essay-length related features\n",
    "usecols = ['word_count', 'token_count', 'unique_token_count', 'nostop_count', 'sent_count']\n",
    "g = sns.pairplot(training_set[training_set.topic == 4], hue='target_score', vars=usecols, plot_kws={\"s\": 20}, palette=\"bright\")\n",
    "g.fig.subplots_adjust(top=.93)\n",
    "g.fig.suptitle('Pairplots of select features', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12976 entries, 0 to 12975\n",
      "Data columns (total 66 columns):\n",
      "essay_id              12976 non-null int64\n",
      "topic                 12976 non-null int64\n",
      "essay                 12976 non-null object\n",
      "rater1_domain1        12976 non-null int64\n",
      "rater2_domain1        12976 non-null int64\n",
      "rater3_domain1        128 non-null float64\n",
      "target_score          12976 non-null int64\n",
      "rater1_domain2        1800 non-null float64\n",
      "rater2_domain2        1800 non-null float64\n",
      "topic2_target         1800 non-null float64\n",
      "rater1_trait1         2292 non-null float64\n",
      "rater1_trait2         2292 non-null float64\n",
      "rater1_trait3         2292 non-null float64\n",
      "rater1_trait4         2292 non-null float64\n",
      "rater1_trait5         723 non-null float64\n",
      "rater1_trait6         723 non-null float64\n",
      "rater2_trait1         2292 non-null float64\n",
      "rater2_trait2         2292 non-null float64\n",
      "rater2_trait3         2292 non-null float64\n",
      "rater2_trait4         2292 non-null float64\n",
      "rater2_trait5         723 non-null float64\n",
      "rater2_trait6         723 non-null float64\n",
      "rater3_trait1         128 non-null float64\n",
      "rater3_trait2         128 non-null float64\n",
      "rater3_trait3         128 non-null float64\n",
      "rater3_trait4         128 non-null float64\n",
      "rater3_trait5         128 non-null float64\n",
      "rater3_trait6         128 non-null float64\n",
      "word_count            12976 non-null int64\n",
      "matches               12976 non-null object\n",
      "corrections           12976 non-null int64\n",
      "corrected             12976 non-null object\n",
      "tokens                12976 non-null object\n",
      "lemma                 12976 non-null object\n",
      "pos                   12976 non-null object\n",
      "sents                 12976 non-null object\n",
      "ner                   12976 non-null object\n",
      "similarity            12976 non-null float64\n",
      "token_count           12976 non-null int64\n",
      "unique_token_count    12976 non-null int64\n",
      "nostop_count          12976 non-null int64\n",
      "sent_count            12976 non-null int64\n",
      "ner_count             12976 non-null int64\n",
      "comma                 12976 non-null int64\n",
      "question              12976 non-null int64\n",
      "exclamation           12976 non-null int64\n",
      "quotation             12976 non-null int64\n",
      "organization          12976 non-null int64\n",
      "caps                  12976 non-null int64\n",
      "person                12976 non-null int64\n",
      "location              12976 non-null int64\n",
      "money                 12976 non-null int64\n",
      "time                  12976 non-null int64\n",
      "date                  12976 non-null int64\n",
      "percent               12976 non-null int64\n",
      "noun                  12976 non-null int64\n",
      "adj                   12976 non-null int64\n",
      "pron                  12976 non-null int64\n",
      "verb                  12976 non-null int64\n",
      "cconj                 12976 non-null int64\n",
      "adv                   12976 non-null int64\n",
      "det                   12976 non-null int64\n",
      "propn                 12976 non-null int64\n",
      "num                   12976 non-null int64\n",
      "part                  12976 non-null int64\n",
      "intj                  12976 non-null int64\n",
      "dtypes: float64(23), int64(35), object(8)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "training_set.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incomplete columns are not used for modeling and can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate feature selection performed on the vectorized data shows few differences in the 10 best features by topic number. It is not surprising that `similarity` has little influence on `target_score` in topic 4 since there are only four unique scores and the the similarity by score plot above shows high variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1</th>\n",
       "      <th>Topic 2</th>\n",
       "      <th>Topic 3</th>\n",
       "      <th>Topic 4</th>\n",
       "      <th>Topic 5</th>\n",
       "      <th>Topic 6</th>\n",
       "      <th>Topic 7</th>\n",
       "      <th>Topic 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>similarity</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>similarity</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>similarity</td>\n",
       "      <td>similarity</td>\n",
       "      <td>similarity</td>\n",
       "      <td>similarity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>unique_token_count</td>\n",
       "      <td>unique_token_count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sent_count</td>\n",
       "      <td>comma</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>ner_count</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>sent_count</td>\n",
       "      <td>sent_count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>comma</td>\n",
       "      <td>noun</td>\n",
       "      <td>comma</td>\n",
       "      <td>comma</td>\n",
       "      <td>comma</td>\n",
       "      <td>ner_count</td>\n",
       "      <td>ner_count</td>\n",
       "      <td>comma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>noun</td>\n",
       "      <td>adj</td>\n",
       "      <td>date</td>\n",
       "      <td>percent</td>\n",
       "      <td>date</td>\n",
       "      <td>comma</td>\n",
       "      <td>noun</td>\n",
       "      <td>noun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>adj</td>\n",
       "      <td>verb</td>\n",
       "      <td>percent</td>\n",
       "      <td>noun</td>\n",
       "      <td>percent</td>\n",
       "      <td>date</td>\n",
       "      <td>adj</td>\n",
       "      <td>adj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>verb</td>\n",
       "      <td>cconj</td>\n",
       "      <td>adj</td>\n",
       "      <td>pron</td>\n",
       "      <td>adj</td>\n",
       "      <td>percent</td>\n",
       "      <td>pron</td>\n",
       "      <td>verb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cconj</td>\n",
       "      <td>adv</td>\n",
       "      <td>pron</td>\n",
       "      <td>verb</td>\n",
       "      <td>pron</td>\n",
       "      <td>adj</td>\n",
       "      <td>verb</td>\n",
       "      <td>adv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>adv</td>\n",
       "      <td>det</td>\n",
       "      <td>verb</td>\n",
       "      <td>cconj</td>\n",
       "      <td>verb</td>\n",
       "      <td>verb</td>\n",
       "      <td>adv</td>\n",
       "      <td>det</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>det</td>\n",
       "      <td>part</td>\n",
       "      <td>cconj</td>\n",
       "      <td>adv</td>\n",
       "      <td>cconj</td>\n",
       "      <td>cconj</td>\n",
       "      <td>det</td>\n",
       "      <td>part</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Topic 1             Topic 2             Topic 3             Topic 4             Topic 5             Topic 6             Topic 7             Topic 8\n",
       "0          similarity  unique_token_count          similarity  unique_token_count          similarity          similarity          similarity          similarity\n",
       "1  unique_token_count          sent_count  unique_token_count          sent_count  unique_token_count  unique_token_count  unique_token_count  unique_token_count\n",
       "2          sent_count               comma          sent_count           ner_count          sent_count          sent_count          sent_count          sent_count\n",
       "3               comma                noun               comma               comma               comma           ner_count           ner_count               comma\n",
       "4                noun                 adj                date             percent                date               comma                noun                noun\n",
       "5                 adj                verb             percent                noun             percent                date                 adj                 adj\n",
       "6                verb               cconj                 adj                pron                 adj             percent                pron                verb\n",
       "7               cconj                 adv                pron                verb                pron                 adj                verb                 adv\n",
       "8                 adv                 det                verb               cconj                verb                verb                 adv                 det\n",
       "9                 det                part               cconj                 adv               cconj               cconj                 det                part"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting k best features: Some features omitted due to high correlation\n",
    "\n",
    "predictors = [  \n",
    "#                 'word_count',\n",
    "                'corrections',\n",
    "                'similarity',\n",
    "#                 'token_count',\n",
    "                'unique_token_count',\n",
    "#                 'nostop_count',\n",
    "                'sent_count',\n",
    "                'ner_count',\n",
    "                'comma',\n",
    "                'question',\n",
    "                'exclamation',\n",
    "                'quotation',\n",
    "                'organization',\n",
    "                'caps',\n",
    "                'person',\n",
    "                'location',\n",
    "                'money',\n",
    "                'time',\n",
    "                'date',\n",
    "                'percent',\n",
    "                'noun',\n",
    "                'adj',\n",
    "                'pron',\n",
    "                'verb',\n",
    "                'cconj',\n",
    "                'adv',\n",
    "                'det',\n",
    "                'propn',\n",
    "                'num',\n",
    "                'part',\n",
    "                'intj'\n",
    "                ]\n",
    "\n",
    "# Create and fit selector\n",
    "selector = SelectKBest(f_regression, k=10) # f_classif, chi2, f_regression, mutual_info_classif, mutual_info_regression\n",
    "\n",
    "# Create empty dataframe\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for topic in range(1, 9):\n",
    "    kpredictors = []\n",
    "    \n",
    "    # test for division by zero errors due to insufficient data:\n",
    "    for p in predictors:\n",
    "        if np.std(training_set[training_set.topic == topic][p], axis=0) != 0:\n",
    "            kpredictors.append(p)\n",
    "            \n",
    "    # select k best for each topic:\n",
    "    X = training_set[training_set.topic == topic][kpredictors]\n",
    "    y = training_set[training_set.topic == topic].target_score\n",
    "    \n",
    "    selector.fit(X, y)\n",
    "\n",
    "    # Get idxs of columns to keep\n",
    "    mask = selector.get_support(indices=True)\n",
    "\n",
    "    selected_features = training_set[training_set.topic == topic][predictors].columns[mask]\n",
    "    df[\"Topic \" + str(topic)] = selected_features\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the regression pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(df, topic, features, model):\n",
    "    \"\"\"Regression pipeline with kappa evaluation\"\"\"\n",
    "\n",
    "    X = df[df['topic'] == topic][features]\n",
    "    y = df[df['topic'] == topic]['target_score'].astype(np.float64)\n",
    "    # token_ct = X.token_count\n",
    "    # X = X.div(token_ct, axis=0)\n",
    "    # X['token_count'] = X['token_count'].mul(token_ct, axis=0)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=26)\n",
    "    \n",
    "    pipeline = Pipeline(model)\n",
    "    pipeline.fit(X_train, y_train)\n",
    "\n",
    "    \n",
    "    y_pred = pipeline.predict(X_test)\n",
    "\n",
    "    return kappa(y_pred, y_test, weights='quadratic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative feature selection strategy is to use **L1** regularization to limit the influence of less important features. This is implemented below in the ElasticNet regressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linearSVC', LinearSVC(C=0.01, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0))]\n",
      "Weighted by topic Kappa score: 0.5751\n",
      "\n",
      "[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('lm', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))]\n",
      "Weighted by topic Kappa score: 0.7100\n",
      "\n",
      "[('rf', RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "           oob_score=False, random_state=26, verbose=0, warm_start=False))]\n",
      "Weighted by topic Kappa score: 0.6898\n",
      "\n",
      "[('en', ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.01,\n",
      "      max_iter=100000, normalize=False, positive=False, precompute=False,\n",
      "      random_state=26, selection='cyclic', tol=0.0001, warm_start=False))]\n",
      "Weighted by topic Kappa score: 0.7055\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictors = [  \n",
    "                'word_count',\n",
    "                'corrections',\n",
    "                'similarity',\n",
    "                'token_count',\n",
    "                'unique_token_count',\n",
    "                'nostop_count',\n",
    "                'sent_count',\n",
    "                'ner_count',\n",
    "                'comma',\n",
    "                'question',\n",
    "                'exclamation',\n",
    "                'quotation',\n",
    "                'organization',\n",
    "                'caps',\n",
    "                'person',\n",
    "                'location',\n",
    "                'money',\n",
    "                'time',\n",
    "                'date',\n",
    "                'percent',\n",
    "                'noun',\n",
    "                'adj',\n",
    "                'pron',\n",
    "                'verb',\n",
    "                'cconj',\n",
    "                'adv',\n",
    "                'det',\n",
    "                'propn',\n",
    "                'num',\n",
    "                'part',\n",
    "                'intj'\n",
    "                ]\n",
    "\n",
    "# feature selection\n",
    "# fvalue_selector = SelectKBest(score_func=f_regression, k=10)\n",
    "\n",
    "# for use in pipeline\n",
    "models = [\n",
    "            [('scaler', StandardScaler()),('linearSVC', LinearSVC(C=0.01))] ,\n",
    "            [('scaler', StandardScaler()),('lm', LinearRegression())], \n",
    "            [('rf', RandomForestRegressor(random_state=26))],  \n",
    "            [('en', ElasticNet(l1_ratio=0.01, alpha=0.1, max_iter=100000, random_state=26))] \n",
    "        ]\n",
    "\n",
    "for steps in models:\n",
    "    kappas = []\n",
    "    weights = []\n",
    "    for topic in range(1,9):\n",
    "        kappas.append(evaluate(training_set, topic, predictors, steps))\n",
    "        weights.append(len(training_set[training_set.topic==topic]))\n",
    "\n",
    "    mqwk = mean_quadratic_weighted_kappa(kappas, weights=weights)\n",
    "    print(steps)\n",
    "    print('Weighted by topic Kappa score: {:.4f}'.format(mqwk))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the four models on which the data was evaluated, the three regression models returned very similar mean weighted kappa scores and the simple linear regression model slightly outperformed the others. The support vector classifier performed poorly. \n",
    "\n",
    "Can we improve on the hyperparameters for ElasticNet by running GridSearchCV on each topic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ElasticNet with GridSearchCV for each individual topic\n",
    "\n",
    "def en_evaluate(df, topic, features):\n",
    "    # Regression pipeline with kappa evaluation\n",
    "    paramgrid = {'l1_ratio': [.01, .1, .3, .5, .7, .99], 'alpha': [0.001, 0.01, 0.1, 1]}\n",
    "    X = df[df['topic'] == topic][features]\n",
    "    y = df[df['topic'] == topic]['target_score'].astype(np.float64)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=26)\n",
    "    \n",
    "    gs = GridSearchCV(ElasticNet(max_iter=100000, random_state=26),\n",
    "                      param_grid=paramgrid,\n",
    "                      cv=5)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Topic', topic, 'best parameters:', gs.best_params_)\n",
    "    y_pred = gs.predict(X_test)\n",
    "\n",
    "    return kappa(y_pred, y_test, weights='quadratic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1 best parameters: {'alpha': 0.001, 'l1_ratio': 0.99}\n",
      "Topic 2 best parameters: {'alpha': 1, 'l1_ratio': 0.01}\n",
      "Topic 3 best parameters: {'alpha': 0.1, 'l1_ratio': 0.5}\n",
      "Topic 4 best parameters: {'alpha': 0.01, 'l1_ratio': 0.7}\n",
      "Topic 5 best parameters: {'alpha': 0.001, 'l1_ratio': 0.99}\n",
      "Topic 6 best parameters: {'alpha': 0.001, 'l1_ratio': 0.01}\n",
      "Topic 7 best parameters: {'alpha': 0.001, 'l1_ratio': 0.7}\n",
      "Topic 8 best parameters: {'alpha': 1, 'l1_ratio': 0.7}\n",
      "Weighted by topic Kappa score: 0.7135\n"
     ]
    }
   ],
   "source": [
    "kappas = []\n",
    "weights = []\n",
    "for topic in range(1,9):\n",
    "    kappas.append(en_evaluate(training_set, topic, predictors))\n",
    "    weights.append(len(training_set[training_set.topic==topic]))\n",
    "    \n",
    "mqwk = mean_quadratic_weighted_kappa(kappas, weights=weights)\n",
    "print('Weighted by topic Kappa score: {:.4f}'.format(mqwk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low `l1_ratio` implies Ridge regression with **l2** regularization. The weighted Kappa score did not improve measurably when hyperparameters are tuned for each topic, including cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8026615360220601,\n",
       " 0.6453843679639755,\n",
       " 0.6675004632203076,\n",
       " 0.6201412784903135,\n",
       " 0.7983501888241695,\n",
       " 0.6810744597077609,\n",
       " 0.733541110264561,\n",
       " 0.7055995750817732]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Individual topic kappa scores\n",
    "kappas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A final approach for feature selection is to extract the Gini-importances of random forests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = training_set[predictors]\n",
    "y = training_set['target_score'].astype(np.float64)\n",
    "\n",
    "\n",
    "forest = ExtraTreesClassifier(n_estimators=250,\n",
    "                              random_state=26)\n",
    "\n",
    "forest.fit(X, y)\n",
    "\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n",
    "\n",
    "# plot feature importances\n",
    "\n",
    "features = pd.DataFrame({'feature_name': X.columns, 'importance': forest.feature_importances_, 'std': std})\n",
    "features.sort_values('importance')\\\n",
    "        .plot.barh(x='feature_name', y='importance', xerr='std', legend=False)\n",
    "plt.title('Gini importances of forest features')\n",
    "plt.xlabel('Gini-importance')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted by topic Kappa score: 0.7014\n"
     ]
    }
   ],
   "source": [
    "# best k features\n",
    "k = 15\n",
    "top_features = features.sort_values('importance', ascending=False)['feature_name'].tolist()[:k]\n",
    "\n",
    "# Linear regression with top k features\n",
    "kappas = []\n",
    "weights = []\n",
    "steps = [('scaler', StandardScaler()),('lm', LinearRegression())]\n",
    "for topic in range(1,9):\n",
    "    kappas.append(evaluate(training_set, topic, top_features, steps))\n",
    "    weights.append(len(training_set[training_set.topic==topic]))\n",
    "\n",
    "mqwk = mean_quadratic_weighted_kappa(kappas, weights=weights)\n",
    "print('Weighted by topic Kappa score: {:.4f}'.format(mqwk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kappa scores increase with increasing number of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown earlier and in the correlation matrix below, some features are highly correlated. This can lead to problems if there are insufficient observations to explain the differences between features. Signs of potential collinearity problems could be poor generalization of the model. In this case, the Kappa scores did not change dramatically when using training and test data or when applying cross-validation.\n",
    "\n",
    "Models that apply feature selection might automatically remove some highly correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overview of correlating features\n",
    "corr = training_set[predictors].corr() # default: Pearson\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "g = sns.heatmap(corr, mask=mask, cmap='Spectral', center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding TF-IDF features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12976, 2000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lemmatized essays re-joined (list to essay)\n",
    "training_set['l_essay'] = training_set['lemma'].apply(' '.join)\n",
    "\n",
    "vectorizer = TfidfVectorizer(max_df=.2, \n",
    "                             min_df=3, \n",
    "                             max_features=2000,\n",
    "                             stop_words=STOP_WORDS) # default: binary=False\n",
    "tfidf_matrix = vectorizer.fit_transform(training_set.l_essay) # using lemmatized essays\n",
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12976, 31)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set[predictors].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12976, 2033)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine previous predictors with TF-IDF matrix\n",
    "combined_dense = pd.concat([pd.DataFrame(tfidf_matrix.todense()), \n",
    "                            training_set[predictors], \n",
    "                            training_set['topic'], \n",
    "                            training_set['target_score']], axis=1)\n",
    "combined_dense.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ElasticNet with GridSearchCV for each individual topic\n",
    "\n",
    "def tf_evaluate(df, topic):\n",
    "    # Regression pipeline with kappa evaluation\n",
    "    paramgrid = {'l1_ratio': [.01, .1, .5, .9], 'alpha': [0.01, .1, 1]}\n",
    "    X = df[df['topic'] == topic].drop(['topic', 'target_score'], axis=1)\n",
    "    y = df[df['topic'] == topic]['target_score'].astype(np.float64)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=26)\n",
    "    \n",
    "    gs = GridSearchCV(ElasticNet(max_iter=100000, random_state=26),\n",
    "                      param_grid=paramgrid,\n",
    "                      cv=5)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Topic', topic, 'best parameters:', gs.best_params_)\n",
    "    y_pred = gs.predict(X_test)\n",
    "\n",
    "    return kappa(y_pred, y_test, weights='quadratic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1 best parameters: {'alpha': 1, 'l1_ratio': 0.1}\n",
      "Topic 2 best parameters: {'alpha': 0.01, 'l1_ratio': 0.01}\n",
      "Topic 3 best parameters: {'alpha': 0.01, 'l1_ratio': 0.01}\n",
      "Topic 4 best parameters: {'alpha': 0.01, 'l1_ratio': 0.01}\n",
      "Topic 5 best parameters: {'alpha': 0.01, 'l1_ratio': 0.01}\n",
      "Topic 6 best parameters: {'alpha': 0.01, 'l1_ratio': 0.01}\n",
      "Topic 7 best parameters: {'alpha': 0.01, 'l1_ratio': 0.9}\n",
      "Topic 8 best parameters: {'alpha': 1, 'l1_ratio': 0.5}\n",
      "Weighted by topic Kappa score: 0.7386\n"
     ]
    }
   ],
   "source": [
    "# ElasticNet with GridSearchCV for each individual topic\n",
    "\n",
    "kappas = []\n",
    "weights = []\n",
    "for topic in range(1,9):\n",
    "    kappas.append(tf_evaluate(combined_dense, topic))\n",
    "    weights.append(len(training_set[training_set.topic==topic]))\n",
    "    \n",
    "mqwk = mean_quadratic_weighted_kappa(kappas, weights=weights)\n",
    "print('Weighted by topic Kappa score: {:.4f}'.format(mqwk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding TF-IDF features only marginally improved the kappa score"
   ]
  }
 ],
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